New Religiosity and the Digital Study of Eudaimonia Data Management Plan
Bibliographic record
Abstract
This project explores under what conditions new religiosity can contribute to personal and societal well-being. Without understanding how specific variables and social dynamics around new religiosity promote or detract from human flourishing, decision making will continue to be largely based on prejudice, assumptions, and outlier examples. It is a partnership between the Open University (UK), The University of British Columbia (Canada) and the independent charity Inform, based at King’s College London (UK). This project is funded by the John Templeton Foundation (no. 63357) from 2025- 2027 and the co-PIs of this project are Suzanne Newcombe and Stephen Christopher.This project will foster increased religious tolerance by unlocking the ability to representatively analyse variables within groups, and between groups and their host societies, which may contribute to or detract from human flourishing and eudaimonia. The project will create the world’s largest, most collaborative, epistemically diverse, open-access mixed data set on new religiosity. The resulting open access infrastructure will allow for new clusters of associations between variables relating to eudaimonia and better reflect the diversity of religiosity globally. This data set will provide an important new resource for researchers, policy makers, and individuals to enable more evidence based decisions about how to engage with new religiosity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".